Deep learning unmasks the ECG signature of Brugada syndrome

Figures

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  Fig. 1.. Representative heartbeats recorded for V1, V2, and V3 in (left) baseline electrocardiograms of three typical patients diagnosed as spontaneous type 1 BrS(+) (top), BrS(+) (middle), and BrS(−) (bottom). Electrocardiograms are recorded during ajmaline administration for patients not presenting a spontaneous type 1 pattern (right). Note the ST-elevation feature signifying BrS in the type 1 ECG traces. In our study, only 17% of BrS(+) patients exhibit a spontaneous type 1 pattern, while the remaining 83% of patients required an ajmaline challenge to reveal a hidden type 1 pattern.

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  Fig. 2.. (A) Flowcharts illustrating the organization of electrocardiograms into one cohort for the purposes of training and validating DNN models, and into a second cohort for independent validation. (B) Distributions of patient clinical statistics, including medical and family history in the Claris training cohort. RBBB refers to right bundle branch block. Complete details can be found in the Supplementary material.

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  Fig. 3.. (A) Flowchart illustrating the ECG data processing workflow from trains of heartbeats as monitored by each ECG lead, to segmentation, outlier discrimination, and averaging to form a set of single representative beats. (B) Structure of the neural network used for training the Brugada syndrome classifier including input, hidden, and output layers. The hidden layer unit includes a fully connected layer followed by Gaussian noise, ReLU activation, and batch normalization. The hidden layer unit is repeated three times, followed by a fully-connected output layer with sigmoid activation. (C) Data partitioning strategy for training a neural network. The overall data partitioning scheme implements leave-one-out cross-validation (LOOCV) to assess the performance of independent neural networks constructed for every patient. For each patient, a 7-fold cross-validation separates the dataset into seven training and validation subsets, each tested on the holdout patient. This analysis yields an average score and standard deviation for each patient across the 7-fold DNNs.

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  Fig. 4.. Receiver–operator characteristic curve for the Claris (training, green, plotted foreground) and Mortara (independent validation, orange, plotted foreground) cohorts.

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  Fig. 5.. DNN feature importance shown on a 9-lead fused ECG trace, representative of the DNN training data. The colormap displays the magnitude of the weights applied by the DNN when predicting ajmaline response. The weights range from 0.0001 to 0.3021; higher–weighted regions have a greater influence on the DNN model.